Technical Note—Managing Nonperishable Inventories with Learning About Demand Arrival Rate Through Stockout Times

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)602-609
Journal / PublicationOperations Research
Issue number3
Online published13 May 2015
Publication statusPublished - May 2015


We study a periodic review inventory model with a nonperishable product over an infinite planning horizon. The demand for the nonperishable product arrives according to a Poisson process. Lost sales are unobservable but the stockout times are observable. We formulate the problem as a dynamic programming model with learning on arrival rate according to stockout times and further simplify it by using unnormalized probabilities. We then compare the system performance with those under other two information scenarios where lost sales are observable or both lost sales and stockout times are unobservable. We show that the optimal inventory order-up-to level with observable stockout times is larger than the one with observable lost sales. We also show that more information improves the system performance.

Research Area(s)

  • information updating, inventory management, Bayesian statistics, nonperishable products